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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/anjalipemmaraju/cyclegan

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/anjandeepsahni/face_classification

Deep Residual Learning for Image Recognition

https://github.com/anjandeepsahni/face_recognition

Deep Residual Learning for Image Recognition

https://github.com/ankit-vaghela30/Google-landmark-prediction

Deep Residual Learning for Image Recognition

https://github.com/anoushkrit/Knowledge

Deep Residual Learning for Image Recognition

https://github.com/antoinedemathelin/wann

Domain-Adversarial Training of Neural Networks

https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/anuraggoel70/Domain-Adversarial-Training-of-Neural-Networks

Domain-Adversarial Training of Neural Networks

https://github.com/aobaruwa/TensorFlow-Projects

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/apple/ml-cvpr2019-swd

Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

https://github.com/arahosu/musicslots

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ariangc/breinchallenge

Deep Residual Learning for Image Recognition

https://github.com/arnab39/cycleGAN-PyTorch

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/arnavdodiedo/dog2sketch

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/arnavdodiedo/sketch2face

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/arobey1/mbrdl

Multimodal Unsupervised Image-to-Image Translation

https://github.com/asahi417/DeepDomainAdaptation

Domain-Adversarial Training of Neural Networks

https://github.com/asarigun/MixerGANsformer

MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation

https://github.com/asbjrnmunk/mdd-unet

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras

Deep Residual Learning for Image Recognition

https://github.com/ashkanpakzad/atn

Learning from Simulated and Unsupervised Images through Adversarial Training

https://github.com/ashura1234/soccer-line-detection

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ashushekar/VGG16

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ashutosh1919/FaceGenerationStyleGAN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/asmerdov/DataCollectionSystem

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

https://github.com/asprenger/keras_fc_densenet

Deep Residual Learning for Image Recognition

https://github.com/asraman9792/Generative-Adversarial-Networks-implementation-of-KERAS

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/astoff/dog-breed-app

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/astoycos/Mini_Project2

Deep Residual Learning for Image Recognition

https://github.com/astro-informatics/rcGAN

A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems

https://github.com/asyrovprog/cs230project

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/attm/tensorflow_advent

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

https://github.com/aurelien-peden/Deep-Learning-paper-implementations

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/avellal14/AI-for-Art

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/avisekiit/wacv_2019

Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.